String metrics have important applications in web search, spelling correction and computational biology amongst others. Many different metrics exist, but the most well-known are based on counting the number of basic edit operations it takes to turn one string into … Continue reading →
Blog Archives
Representation of numerical NA’s in R and the 1954 enigma
I've always wondered how exactly the missing value (NA) in R is represented under the hood. Last weekend I was working on a little project that gave me enough excuse to spend some time on finding this out. So, I … Continue reading →
Deductive imputation with the deducorrect package
Missing data hinders statistical analyses. Estimating missing values (imputation) prior to analysis is one way to deal with that. In some cases however, the missings need not be estimated at all, since they can be derived with certainty from other … Continue reading →
What do your rules look like? editrules 1.8-x answers with the help of igraph
We (Edwin de Jonge and me) have recently updated our editrules package. The most important new features include (beta) support for categorical data. However, in this post I'm going to show some visualizations we included, made possible by Gabor Csardi's … Continue reading →
A multidimensional “which” function
update Henrik Bengtsson commented that which(x, arr.ind=TRUE) gives the same result, rendering the blog below academic (thanks for the comment!). So, for academic interest, I'll leave it. In my defense, I implemented this kind of functionality in C some time … Continue reading →
Fourier-Motzkin elimination with the editrules package
Last week I talked about our editrules package at the useR!2011 conference. In the coming time I plan to write a short series of blogs about the functionality of editrules. Below I describe the eliminate and isFeasible functions. But first: … Continue reading →
useR!2011
useR!2011 ended yesterday. First of all, much thanks to the organizers who managed to run a conference with 400+ participants, from 41 countries smoothly. Thumbs up! It was great to meet some people from the R blog-O-sphere in person, like … Continue reading →
Improving data quality with deducorrect
Does your raw numerical data suffer from typos? sign errors? variable swaps? rounding errors? You may be able to fix all that with the deducorrect package. Today, we (that is Edwin de Jonge, Sander Scholtus and myself) uploaded the, 1.0-0 … Continue reading →
Simple pattern detection in numerical data
A while ago I helped out a colleague who was testing some methods to detect economic (in)activity of companies, based on their quarterly tax declarations (our institute has access to such data). One of the thing my colleague wanted was to detect simple patterns, … Continue reading →
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
